system

Abstract

The system according to this embodiment aims to automate tasks by generating scripts based on voice commands, even without programming knowledge. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an execution unit, and a provision unit. The reception unit receives voice instructions. The generation unit analyzes the voice instructions received by the reception unit and generates a script. The execution unit executes the script generated by the generation unit. The provision unit provides the results executed by the execution unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for users without programming knowledge to automatically generate scripts and automate operations.

[0005] The system according to the embodiment aims to generate a script by voice instruction even without programming knowledge and automate operations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an execution unit, and a provision unit. The reception unit receives voice instructions. The generation unit analyzes the voice instructions received by the reception unit and generates a script. The execution unit executes the script generated by the generation unit. The provision unit provides the results executed by the execution unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate scripts based on voice commands and automate tasks without requiring any programming knowledge. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The office automation system according to an embodiment of the present invention is a system that automates office tasks by automatically generating scripts based on voice commands using a generative AI, even without programming knowledge. The office automation system works by the user giving voice commands. For example, the user might say, "Summarize the data in the spreadsheet and create a graph." This command is input to the generative AI. Next, the generative AI analyzes the input command and automatically generates a script. Based on the user's command, the generative AI generates a script that aggregates the data in the spreadsheet and creates a graph. The generated script is automatically executed by the generative AI. For example, if the generated script aggregates the data in the spreadsheet and creates a graph, the results are automatically displayed. Furthermore, the generative AI confirms the execution results of the script in a dialogue format with the user and accepts instructions for correction or additional work as needed. For example, if the user says, "Change the color of this graph," the generative AI corrects the script based on that instruction and executes it again. This mechanism allows users to automate office tasks in a dialogue format using voice commands, even without programming knowledge. This improves work efficiency and makes it easier to utilize generative AI in business operations. This allows the office automation system to automatically generate scripts based on user voice commands, enabling efficient automation of office tasks.

[0029] The office automation system according to this embodiment comprises a reception unit, a generation unit, an execution unit, and a provision unit. The reception unit receives voice instructions. For example, a user can give a voice instruction such as "Summarize the data in the spreadsheet and create a graph." The reception unit converts the voice instructions into text data using speech recognition technology. The generation unit analyzes the voice instructions received by the reception unit and generates a script. For example, the generation unit uses a generation AI to analyze the voice instructions and generate a script that aggregates the data in the spreadsheet and creates a graph. The generation unit generates a script when the generation AI receives the prompt "Summarize the data in the spreadsheet and create a graph." The execution unit executes the script generated by the generation unit. For example, the execution unit executes the generated script, aggregates the data in the spreadsheet, and creates a graph. The provision unit provides the results executed by the execution unit. For example, the provision unit displays the execution results of the generated script to the user. The provision unit analyzes the execution results using a generation AI and provides feedback to the user. As a result, the office automation system according to this embodiment can automate office tasks by receiving voice commands, generating scripts, executing them, and providing the results.

[0030] The reception unit receives voice commands. For example, a user can give a voice command such as, "Summarize the data in the spreadsheet and create a graph." The reception unit uses speech recognition technology to convert the voice commands into text data. Specifically, the speech recognition technology uses advanced natural language processing (NLP) algorithms to convert the user's voice into text with high accuracy. The speech recognition engine takes into account background noise, speaker accent, and variations in speech speed to generate accurate text data. Furthermore, to understand the context of the voice commands, the speech recognition engine uses a large, pre-trained speech dataset and has the ability to recognize specific business terms and phrases. For example, specialized terms such as "spreadsheet" and "graph creation" are accurately recognized. Upon receiving a voice command, the reception unit immediately sends it to the speech recognition engine, and the converted text data is passed to the generation unit. This process is performed in real time, allowing the user to proceed to the next step without waiting.

[0031] The generation unit analyzes the voice instructions received by the reception unit and generates a script. For example, the generation unit uses a generation AI to analyze the voice instructions and generate a script that aggregates data from a spreadsheet and creates a graph. The generation unit receives a prompt from the generation AI such as "aggregate the data from the spreadsheet and create a graph," and generates a script. Specifically, the generation AI uses natural language processing (NLP) technology to analyze the meaning of the voice instructions and generates an appropriate script. Based on a large dataset that has been pre-trained on, the generation AI understands the user's instructions and identifies the necessary operating procedures. For example, if it receives the instruction "aggregate the data from the spreadsheet and create a graph," the generation AI first reads the data from the spreadsheet, then determines how to aggregate the data, and finally generates a script outlining the steps to create the graph. The generated script is written in a programming language such as Python or JavaScript (registered trademark) and is in a format suitable for execution by the execution unit. The generation unit passes the generated script to the execution unit and proceeds to the next step.

[0032] The execution unit executes the script generated by the generation unit. For example, the execution unit executes the generated script, aggregates data from a spreadsheet, and creates a graph. Specifically, the execution unit receives the script and executes each step within the script sequentially. The script reads the data from the spreadsheet, processes the data according to the specified aggregation method, and finally creates a graph. The execution unit also has the capability to monitor and appropriately handle any errors or exceptions that may occur during script execution. For example, if the spreadsheet data is incomplete or the specified aggregation method cannot be applied, the execution unit generates an error message and notifies the user. The execution unit also temporarily saves the results of the script execution and prepares to pass them to the delivery unit. This ensures that the execution unit executes the script reliably and provides accurate results.

[0033] The service provider provides the results executed by the execution unit. For example, the service provider displays the execution results of a generated script to the user. Specifically, the service provider presents the result data received from the execution unit to the user in a visually easy-to-understand format. For example, it displays the aggregated results of a spreadsheet or the created graphs on the user's device. The service provider analyzes the execution results using a generation AI and provides feedback to the user. For example, the generation AI can evaluate the execution results, detect data trends and anomalies, and suggest additional actions to the user. The service provider designs the interface and display methods to make it easy for the user to check the results. For example, it can customize the color and shape of graphs and provide a function to display data details in a pop-up. The service provider can also link the results with other systems and applications, allowing users to utilize the results in other business processes. In this way, the service provider can effectively provide execution results to the user and support the efficiency of office work.

[0034] The generation unit can generate a script that aggregates data from a spreadsheet and creates a graph. For example, the generation unit generates a script that aggregates data from a spreadsheet and creates a graph. The generation unit uses a generation AI to generate a script that aggregates data from a spreadsheet and creates a graph. For example, the generation unit generates a script when the generation AI receives a prompt such as "aggregate the data from the spreadsheet and create a graph." This makes data processing more efficient by automatically generating a script that aggregates data from a spreadsheet and creates a graph. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs a prompt to the generation AI in order to generate a script that aggregates data from a spreadsheet and creates a graph, and the generation AI generates the script.

[0035] The generation unit may include a modification unit that modifies the script based on user instructions. For example, if the user instructs the generation unit to "change the color of this graph," the generation unit modifies the script based on that instruction. The generation unit modifies the script based on user instructions using a generation AI. For example, the generation AI receives a prompt such as "change the color of this graph" and modifies the script. This allows for flexible responses by modifying the script based on user instructions. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit inputs a prompt to the generation AI in order to modify the script based on user instructions, and the generation AI modifies the script.

[0036] The reception unit may include an additional instruction reception unit to receive additional instructions. For example, the user may give additional instructions to the reception unit, such as "Further filter this data." The reception unit uses speech recognition technology to convert the additional voice instructions into text data. This allows for flexible responses to meet the user's needs by accepting additional instructions. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit may input a prompt to the generative AI to receive additional instructions, and the generative AI will analyze the additional instructions.

[0037] The service provider can provide the user with the execution results of the generated script. For example, the service provider can display the execution results of the generated script to the user. The service provider can analyze the execution results using a generation AI and provide feedback to the user. This makes it easier for the user to check the results and provide feedback by providing the user with the execution results of the generated script. Some or all of the above processing in the service provider may be performed using a generation AI or not. For example, the service provider can input the execution results of the generated script into a generation AI, and the generation AI can analyze the execution results and provide them to the user.

[0038] The reception unit can analyze the user's past voice command history and select the optimal reception method. For example, the reception unit's generating AI proposes the optimal reception method based on patterns of voice commands frequently used by the user in the past. The reception unit's generating AI selects a reception method suitable for a specific time period from the user's past voice command history. The reception unit analyzes the user's past voice command history and provides a reception method tailored to the user's preferences. In this way, the reception unit can provide the user with the optimal reception method by analyzing the past voice command history. Some or all of the above processing in the reception unit may be performed using the generating AI or not. For example, the reception unit inputs the user's past voice command history into the generating AI, and the generating AI selects the optimal reception method.

[0039] The reception unit can filter voice commands based on the user's current work status and areas of interest. For example, the reception unit's generating AI prioritizes receiving only voice commands related to the user's current work. The reception unit uses the generating AI to filter and receive highly relevant voice commands based on the user's areas of interest. The reception unit monitors the user's work status in real time, and the generating AI selects and receives appropriate voice commands. This allows for the priority of receiving highly relevant commands by filtering voice commands based on the user's work status and areas of interest. Some or all of the above processing in the reception unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the reception unit inputs voice commands to the generating AI based on the user's work status and areas of interest, and the generating AI performs the filtering.

[0040] The reception unit can prioritize receiving voice instructions by considering the user's geographical location information. For example, if the user is in the office, the generating AI will prioritize receiving voice instructions related to office work. If the user is out of the office, the generating AI will prioritize receiving voice instructions related to work at the location. Based on the user's geographical location information, the generating AI will prioritize receiving voice instructions needed at that location. This allows for the priority of receiving highly relevant instructions by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the reception unit inputs the user's geographical location information into the generating AI, and the generating AI selects highly relevant instructions.

[0041] The reception unit can analyze the user's social media activity when receiving voice instructions and receive relevant instructions. For example, the reception unit's generative AI can identify the user's current interests from their social media activity and prioritize receiving relevant voice instructions. The reception unit's generative AI receives relevant work instructions based on information shared by the user on social media. The reception unit analyzes the user's social media activity and the generative AI receives voice instructions related to that activity. This allows the reception unit to prioritize receiving highly relevant instructions by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using the generative AI or not. For example, the reception unit inputs the user's social media activity into the generative AI, and the generative AI selects relevant instructions.

[0042] The generation unit can adjust the level of detail of the generated script based on the importance of the instructions. For example, the generation unit's generation AI generates a detailed script for high-importance instructions. For low-importance instructions, the generation AI generates a concise script. The generation unit dynamically adjusts the level of detail of the script according to the importance of the instructions. This enables efficient script generation by adjusting the level of detail of the script based on the importance of the instructions. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without using the generation AI. For example, the generation unit inputs the importance of the instructions to the generation AI, and the generation AI adjusts the level of detail of the script.

[0043] The generation unit can apply different generation algorithms depending on the instruction category when generating scripts. For example, the generation unit can generate a script by applying a specific algorithm to a data aggregation instruction. The generation unit can generate a script by applying a different algorithm to a graph creation instruction. The generation unit can generate a script by having the generation AI select the optimal generation algorithm according to the instruction category. This enables efficient script generation by applying the optimal generation algorithm according to the instruction category. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit inputs the instruction category to the generation AI, and the generation AI selects the optimal generation algorithm.

[0044] The generation unit can determine the priority of script generation based on the instruction submission timing. For example, the generation unit's generation AI prioritizes script generation for urgent instructions. The generation unit's generation AI prioritizes script generation for instructions with approaching submission deadlines. The generation unit's generation AI dynamically adjusts the script generation priority based on the instruction submission timing. This enables efficient script generation by determining the script generation priority based on the instruction submission timing. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the generation unit inputs the instruction submission timing to the generation AI, and the generation AI determines the script generation priority.

[0045] The generation unit can adjust the script generation order based on the relevance of the instructions during script generation. For example, the generation unit's generation AI prioritizes script generation for highly relevant instructions. The generation unit's generation AI postpones script generation for less relevant instructions. The generation unit's generation AI dynamically adjusts the script generation order based on the relevance of the instructions. This allows for efficient script generation by adjusting the script generation order based on the relevance of the instructions. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the generation unit inputs the relevance of the instructions to the generation AI, and the generation AI adjusts the script generation order.

[0046] The execution unit can select the optimal execution method by referring to past execution results when executing a script. For example, the execution unit's generating AI selects the optimal execution method based on past successful execution methods. The execution unit's generating AI improves the execution method by referring to past failures. The execution unit analyzes past execution results and the generating AI selects the most efficient execution method. In this way, the optimal execution method can be selected by referring to past execution results. Some or all of the above processes in the execution unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the execution unit inputs past execution results into the generating AI, and the generating AI selects the optimal execution method.

[0047] The execution unit can customize the execution method based on the user's current work situation when executing a script. For example, if the user is busy, the execution unit provides a means that can be executed quickly by the generating AI. If the user is relaxed, the execution unit provides an execution method that includes detailed instructions by the generating AI. The execution unit understands the user's work situation in real time, and the generating AI selects the appropriate execution method. This enables efficient execution by customizing the execution method based on the user's work situation. Some or all of the above processing in the execution unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the execution unit inputs the user's work situation into the generating AI, and the generating AI selects the optimal execution method.

[0048] The execution unit can select the optimal execution method when executing a script, taking into account the user's geographical location information. For example, if the user is in the office, the generating AI will prioritize executing scripts related to office work. If the user is out of the office, the generating AI will prioritize executing scripts related to work performed outside the office. Based on the user's geographical location information, the generating AI will prioritize executing scripts required at that location. In this way, the optimal execution method can be selected by taking the user's geographical location information into consideration. Some or all of the above processing in the execution unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the execution unit inputs the user's geographical location information into the generating AI, and the generating AI selects the optimal execution method.

[0049] The execution unit can analyze the user's social media activity and suggest execution methods when executing a script. For example, the execution unit uses the user's social media activity to determine the user's current interests, and the generating AI prioritizes the execution of relevant scripts. The execution unit uses the information shared by the user on social media to execute relevant business scripts using the generating AI. The execution unit analyzes the user's social media activity, and the generating AI executes scripts related to that activity. In this way, by analyzing the user's social media activity, it can suggest highly relevant execution methods. Some or all of the above processing in the execution unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the execution unit inputs the user's social media activity into the generating AI, and the generating AI suggests the optimal execution method.

[0050] The service provider can select the optimal service method by referring to the user's past operation history when providing execution results. For example, the service provider's generating AI selects the optimal service method based on the service method the user has preferred to use in the past. The service provider's generating AI selects a service method suitable for a specific time period from the user's past operation history. The service provider analyzes the user's past operation history, and the generating AI provides a service method tailored to the user's preferences. This allows the service provider to select the optimal service method by referring to the user's past operation history. Some or all of the above-described processes in the service provider may be performed using the generating AI, or they may be performed without the generating AI. For example, the service provider inputs the user's past operation history into the generating AI, and the generating AI selects the optimal service method.

[0051] The delivery unit can customize the means of delivery based on the user's current work situation when providing execution results. For example, if the user is busy, the delivery unit provides a means of delivery that the generating AI can quickly understand. If the user is relaxed, the delivery unit provides a means of delivery that includes detailed instructions. The delivery unit grasps the user's work situation in real time, and the generating AI selects the appropriate means of delivery. This enables efficient delivery by customizing the means of delivery based on the user's work situation. Some or all of the above processing in the delivery unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the delivery unit inputs the user's work situation into the generating AI, and the generating AI selects the optimal means of delivery.

[0052] The service provider can select the optimal delivery method when providing execution results, taking into account the user's geographical location information. For example, if the user is in the office, the generating AI will prioritize providing execution results related to office work. If the user is out of the office, the generating AI will prioritize providing execution results related to work performed while out of the office. Based on the user's geographical location information, the generating AI will prioritize providing execution results required at that location. This allows the service provider to select the optimal delivery method by taking the user's geographical location information into consideration. Some or all of the above processing in the service provider may be performed using the generating AI, or it may be performed without using the generating AI. For example, the service provider can input the user's geographical location information into the generating AI, and the generating AI will select the optimal delivery method.

[0053] The service provider can analyze the user's social media activity and propose a means of delivery when providing execution results. For example, the service provider's generating AI can understand the user's current interests from their social media activity and prioritize providing relevant execution results. The service provider's generating AI provides relevant business execution results based on information shared by the user on social media. The service provider analyzes the user's social media activity and the generating AI provides execution results related to that activity. In this way, by analyzing the user's social media activity, it is possible to propose a highly relevant means of delivery. Some or all of the above processing in the service provider may be performed using the generating AI or not. For example, the service provider inputs the user's social media activity into the generating AI, and the generating AI proposes the optimal means of delivery.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The office automation system can also include a history analysis unit that analyzes the user's past work history. For example, the history analysis unit analyzes patterns in the user's past work, and the generation unit generates the optimal script based on those patterns. The execution unit can select the most efficient execution method based on the past work history. The provision unit can provide optimal feedback to the user, referencing the past work history. This enables more efficient business automation by leveraging past work history.

[0056] The office automation system can further include a location information acquisition unit that obtains the user's geographical location information. For example, if the user is in the office, the location information acquisition unit can prioritize generating scripts related to office tasks using AI. If the user is out of the office, the execution unit can prioritize executing scripts related to tasks performed outside the office. The provision unit can provide optimal feedback based on the user's geographical location information. This enables more appropriate task automation by taking the user's geographical location into consideration.

[0057] The office automation system can also include a work status monitoring unit that grasps the user's work status in real time. For example, if the user is busy, the AI ​​generates a script that can be executed quickly. The execution unit can then execute the script, including detailed instructions, when the user is relaxed. The delivery unit can provide optimal feedback based on the user's work status. This enables more appropriate task automation according to the user's work situation.

[0058] The office automation system can also include a social media analysis unit that analyzes users' social media activity. For example, the social media analysis unit uses a generation AI to understand users' current interests from their social media activity and generate relevant scripts. The execution unit can then execute the relevant business scripts based on the information shared by the user on social media. The delivery unit can analyze users' social media activity and provide optimal feedback. This enables more appropriate business automation by leveraging users' social media activity.

[0059] The office automation system can also include an operation history analysis unit that analyzes the user's past operation history. For example, the operation history analysis unit uses a generating AI to select the optimal delivery method based on the user's preferred delivery methods in the past. The execution unit can select an execution method suitable for a specific time period based on the user's past operation history. The delivery unit can analyze the user's past operation history and provide optimal feedback. This enables more efficient business automation by leveraging the user's past operation history.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives voice instructions. For example, a user can give a voice instruction such as, "Summarize the data in the spreadsheet and create a graph." The reception desk uses speech recognition technology to convert the voice instructions into text data. Step 2: The generation unit analyzes the voice instructions received by the reception unit and generates a script. For example, the generation unit uses a generation AI to analyze the voice instructions and generate a script that aggregates data from a spreadsheet and creates a graph. The generation unit generates a script when the generation AI receives the prompt, "Aggregate the data from the spreadsheet and create a graph." Step 3: The execution unit executes the script generated by the generation unit. For example, the execution unit executes the generated script, aggregates the data in the spreadsheet, and creates a graph. Step 4: The provider unit provides the results executed by the execution unit. For example, the provider unit displays the execution results of the generated script to the user. The provider unit analyzes the execution results using the generation AI and provides feedback to the user.

[0062] (Example of form 2) The office automation system according to an embodiment of the present invention is a system that automates office tasks by automatically generating scripts based on voice commands using a generative AI, even without programming knowledge. The office automation system works by the user giving voice commands. For example, the user might say, "Summarize the data in the spreadsheet and create a graph." This command is input to the generative AI. Next, the generative AI analyzes the input command and automatically generates a script. Based on the user's command, the generative AI generates a script that aggregates the data in the spreadsheet and creates a graph. The generated script is automatically executed by the generative AI. For example, if the generated script aggregates the data in the spreadsheet and creates a graph, the results are automatically displayed. Furthermore, the generative AI confirms the execution results of the script in a dialogue format with the user and accepts instructions for correction or additional work as needed. For example, if the user says, "Change the color of this graph," the generative AI corrects the script based on that instruction and executes it again. This mechanism allows users to automate office tasks in a dialogue format using voice commands, even without programming knowledge. This improves work efficiency and makes it easier to utilize generative AI in business operations. This allows the office automation system to automatically generate scripts based on user voice commands, enabling efficient automation of office tasks.

[0063] The office automation system according to this embodiment comprises a reception unit, a generation unit, an execution unit, and a provision unit. The reception unit receives voice instructions. For example, a user can give a voice instruction such as "Summarize the data in the spreadsheet and create a graph." The reception unit converts the voice instructions into text data using speech recognition technology. The generation unit analyzes the voice instructions received by the reception unit and generates a script. For example, the generation unit uses a generation AI to analyze the voice instructions and generate a script that aggregates the data in the spreadsheet and creates a graph. The generation unit generates a script when the generation AI receives the prompt "Summarize the data in the spreadsheet and create a graph." The execution unit executes the script generated by the generation unit. For example, the execution unit executes the generated script, aggregates the data in the spreadsheet, and creates a graph. The provision unit provides the results executed by the execution unit. For example, the provision unit displays the execution results of the generated script to the user. The provision unit analyzes the execution results using a generation AI and provides feedback to the user. As a result, the office automation system according to this embodiment can automate office tasks by receiving voice commands, generating scripts, executing them, and providing the results.

[0064] The reception unit receives voice commands. For example, a user can give a voice command such as, "Summarize the data in the spreadsheet and create a graph." The reception unit uses speech recognition technology to convert the voice commands into text data. Specifically, the speech recognition technology uses advanced natural language processing (NLP) algorithms to convert the user's voice into text with high accuracy. The speech recognition engine takes into account background noise, speaker accent, and variations in speech speed to generate accurate text data. Furthermore, to understand the context of the voice commands, the speech recognition engine uses a large, pre-trained speech dataset and has the ability to recognize specific business terms and phrases. For example, specialized terms such as "spreadsheet" and "graph creation" are accurately recognized. Upon receiving a voice command, the reception unit immediately sends it to the speech recognition engine, and the converted text data is passed to the generation unit. This process is performed in real time, allowing the user to proceed to the next step without waiting.

[0065] The generation unit analyzes the voice instructions received by the reception unit and generates a script. For example, the generation unit uses a generation AI to analyze the voice instructions and generate a script that aggregates data from a spreadsheet and creates a graph. The generation unit receives a prompt from the generation AI such as "aggregate the data from the spreadsheet and create a graph," and generates a script. Specifically, the generation AI uses natural language processing (NLP) techniques to analyze the meaning of the voice instructions and generates an appropriate script. Based on a large dataset that has been pre-trained, the generation AI understands the user's instructions and identifies the necessary operating procedures. For example, if it receives the instruction "aggregate the data from the spreadsheet and create a graph," the generation AI will first read the data from the spreadsheet, then determine how to aggregate the data, and finally generate a script outlining the steps to create the graph. The generated script is written in a programming language such as Python or JavaScript and is in a format suitable for execution by the execution unit. The generation unit passes the generated script to the execution unit and proceeds to the next step.

[0066] The execution unit executes the script generated by the generation unit. For example, the execution unit executes the generated script, aggregates data from a spreadsheet, and creates a graph. Specifically, the execution unit receives the script and executes each step within the script sequentially. The script reads the data from the spreadsheet, processes the data according to the specified aggregation method, and finally creates a graph. The execution unit also has the capability to monitor and appropriately handle any errors or exceptions that may occur during script execution. For example, if the spreadsheet data is incomplete or the specified aggregation method cannot be applied, the execution unit generates an error message and notifies the user. The execution unit also temporarily saves the results of the script execution and prepares to pass them to the delivery unit. This ensures that the execution unit executes the script reliably and provides accurate results.

[0067] The service provider provides the results executed by the execution unit. For example, the service provider displays the execution results of a generated script to the user. Specifically, the service provider presents the result data received from the execution unit to the user in a visually easy-to-understand format. For example, it displays the aggregated results of a spreadsheet or the created graphs on the user's device. The service provider analyzes the execution results using a generation AI and provides feedback to the user. For example, the generation AI can evaluate the execution results, detect data trends and anomalies, and suggest additional actions to the user. The service provider designs the interface and display methods to make it easy for the user to check the results. For example, it can customize the color and shape of graphs and provide a function to display data details in a pop-up. The service provider can also link the results with other systems and applications, allowing users to utilize the results in other business processes. In this way, the service provider can effectively provide execution results to the user and support the efficiency of office work.

[0068] The generation unit can generate a script that aggregates data from a spreadsheet and creates a graph. For example, the generation unit generates a script that aggregates data from a spreadsheet and creates a graph. The generation unit uses a generation AI to generate a script that aggregates data from a spreadsheet and creates a graph. For example, the generation unit generates a script when the generation AI receives a prompt such as "aggregate the data from the spreadsheet and create a graph." This makes data processing more efficient by automatically generating a script that aggregates data from a spreadsheet and creates a graph. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs a prompt to the generation AI in order to generate a script that aggregates data from a spreadsheet and creates a graph, and the generation AI generates the script.

[0069] The generation unit may include a modification unit that modifies the script based on user instructions. For example, if the user instructs the generation unit to "change the color of this graph," the generation unit modifies the script based on that instruction. The generation unit modifies the script based on user instructions using a generation AI. For example, the generation AI receives a prompt such as "change the color of this graph" and modifies the script. This allows for flexible responses by modifying the script based on user instructions. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit inputs a prompt to the generation AI in order to modify the script based on user instructions, and the generation AI modifies the script.

[0070] The reception unit may include an additional instruction reception unit to receive additional instructions. For example, the user may give additional instructions to the reception unit, such as "Further filter this data." The reception unit uses speech recognition technology to convert the additional voice instructions into text data. This allows for flexible responses to meet the user's needs by accepting additional instructions. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit may input a prompt to the generative AI to receive additional instructions, and the generative AI will analyze the additional instructions.

[0071] The service provider can provide the user with the execution results of the generated script. For example, the service provider can display the execution results of the generated script to the user. The service provider can analyze the execution results using a generation AI and provide feedback to the user. This makes it easier for the user to check the results and provide feedback by providing the user with the execution results of the generated script. Some or all of the above processing in the service provider may be performed using a generation AI or not. For example, the service provider can input the execution results of the generated script into a generation AI, and the generation AI can analyze the execution results and provide them to the user.

[0072] The reception unit can estimate the user's emotions and adjust the timing of voice command reception based on the estimated emotions. For example, if the user is stressed, the reception unit's generating AI will delay the reception of voice commands, waiting until the user is relaxed. If the user is relaxed, the reception unit's generating AI will quickly receive voice commands, facilitating smoother dialogue. If the user is in a hurry, the reception unit's generating AI will immediately receive voice commands, enabling a quick response. This allows for smoother dialogue by adjusting the timing of voice command reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generating AI. For example, the reception unit inputs user emotion data into a generating AI, which estimates the emotions and adjusts the timing of voice command reception.

[0073] The reception unit can analyze the user's past voice command history and select the optimal reception method. For example, the reception unit's generating AI proposes the optimal reception method based on patterns of voice commands frequently used by the user in the past. The reception unit's generating AI selects a reception method suitable for a specific time period from the user's past voice command history. The reception unit analyzes the user's past voice command history and provides a reception method tailored to the user's preferences. In this way, the reception unit can provide the user with the optimal reception method by analyzing the past voice command history. Some or all of the above processing in the reception unit may be performed using the generating AI or not. For example, the reception unit inputs the user's past voice command history into the generating AI, and the generating AI selects the optimal reception method.

[0074] The reception unit can filter voice commands based on the user's current work status and areas of interest. For example, the reception unit's generating AI prioritizes receiving only voice commands related to the user's current work. The reception unit uses the generating AI to filter and receive highly relevant voice commands based on the user's areas of interest. The reception unit monitors the user's work status in real time, and the generating AI selects and receives appropriate voice commands. This allows for the priority of receiving highly relevant commands by filtering voice commands based on the user's work status and areas of interest. Some or all of the above processing in the reception unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the reception unit inputs voice commands to the generating AI based on the user's work status and areas of interest, and the generating AI performs the filtering.

[0075] The reception unit can estimate the user's emotions and determine the priority of voice instructions to receive based on the estimated emotions. For example, if the user is stressed, the generating AI will postpone less important voice instructions and prioritize simple ones. If the user is relaxed, the generating AI will prioritize receiving high-importance voice instructions. If the user is in a hurry, the generating AI will immediately receive urgent voice instructions. This allows for a more appropriate response by prioritizing voice instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generating AI. For example, the reception unit inputs user emotion data into a generating AI, which estimates the emotions and determines the priority of voice instructions.

[0076] The reception unit can prioritize receiving voice instructions by considering the user's geographical location information. For example, if the user is in the office, the generating AI will prioritize receiving voice instructions related to office work. If the user is out of the office, the generating AI will prioritize receiving voice instructions related to work at the location. Based on the user's geographical location information, the generating AI will prioritize receiving voice instructions needed at that location. This allows for the priority of receiving highly relevant instructions by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the reception unit inputs the user's geographical location information into the generating AI, and the generating AI selects highly relevant instructions.

[0077] The reception unit can analyze the user's social media activity when receiving voice instructions and receive relevant instructions. For example, the reception unit's generative AI can identify the user's current interests from their social media activity and prioritize receiving relevant voice instructions. The reception unit's generative AI receives relevant work instructions based on information shared by the user on social media. The reception unit analyzes the user's social media activity and the generative AI receives voice instructions related to that activity. This allows the reception unit to prioritize receiving highly relevant instructions by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using the generative AI or not. For example, the reception unit inputs the user's social media activity into the generative AI, and the generative AI selects relevant instructions.

[0078] The generation unit can estimate the user's emotions and adjust the script generation method based on the estimated emotions. For example, if the user is relaxed, the generation AI will generate a detailed script. If the user is in a hurry, the generation AI will generate a concise and quickly executable script. If the user is stressed, the generation AI will generate a simple and easy-to-understand script. This allows for the generation of more appropriate scripts by adjusting the script generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit inputs user emotion data into the generation AI, which estimates the emotions and adjusts the script generation method.

[0079] The generation unit can adjust the level of detail of the generated script based on the importance of the instructions. For example, the generation unit's generation AI generates a detailed script for high-importance instructions. For low-importance instructions, the generation AI generates a concise script. The generation unit dynamically adjusts the level of detail of the script according to the importance of the instructions. This enables efficient script generation by adjusting the level of detail of the script based on the importance of the instructions. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without using the generation AI. For example, the generation unit inputs the importance of the instructions to the generation AI, and the generation AI adjusts the level of detail of the script.

[0080] The generation unit can apply different generation algorithms depending on the instruction category when generating scripts. For example, the generation unit can generate a script by applying a specific algorithm to a data aggregation instruction. The generation unit can generate a script by applying a different algorithm to a graph creation instruction. The generation unit can generate a script by having the generation AI select the optimal generation algorithm according to the instruction category. This enables efficient script generation by applying the optimal generation algorithm according to the instruction category. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit inputs the instruction category to the generation AI, and the generation AI selects the optimal generation algorithm.

[0081] The generation unit can estimate the user's emotions and adjust the length of the generated script based on the estimated emotions. For example, if the user is in a hurry, the generation AI will generate a short, concise script. If the user is relaxed, the generation AI will generate a longer script with detailed explanations. If the user is stressed, the generation AI will generate a simple, short script. This allows for the generation of more appropriate scripts by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs user emotion data into the generation AI, which estimates the emotions and adjusts the length of the script.

[0082] The generation unit can determine the priority of script generation based on the instruction submission timing. For example, the generation unit's generation AI prioritizes script generation for urgent instructions. The generation unit's generation AI prioritizes script generation for instructions with approaching submission deadlines. The generation unit's generation AI dynamically adjusts the script generation priority based on the instruction submission timing. This enables efficient script generation by determining the script generation priority based on the instruction submission timing. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the generation unit inputs the instruction submission timing to the generation AI, and the generation AI determines the script generation priority.

[0083] The generation unit can adjust the script generation order based on the relevance of the instructions during script generation. For example, the generation unit's generation AI prioritizes script generation for highly relevant instructions. The generation unit's generation AI postpones script generation for less relevant instructions. The generation unit's generation AI dynamically adjusts the script generation order based on the relevance of the instructions. This allows for efficient script generation by adjusting the script generation order based on the relevance of the instructions. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the generation unit inputs the relevance of the instructions to the generation AI, and the generation AI adjusts the script generation order.

[0084] The execution unit can estimate the user's emotions and adjust how the script is executed based on the estimated emotions. For example, if the user is relaxed, the execution unit can have the generative AI provide an execution method that includes detailed steps. If the user is in a hurry, the execution unit can have the generative AI provide a method that can be executed quickly. If the user is stressed, the execution unit can have the generative AI provide a simple and easy-to-understand execution method. This allows for more appropriate execution by adjusting how the script is executed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using the generative AI or not. For example, the execution unit inputs user emotion data into the generative AI, which estimates the emotions and adjusts how the script is executed.

[0085] The execution unit can select the optimal execution method by referring to past execution results when executing a script. For example, the execution unit's generating AI selects the optimal execution method based on past successful execution methods. The execution unit's generating AI improves the execution method by referring to past failures. The execution unit analyzes past execution results and the generating AI selects the most efficient execution method. In this way, the optimal execution method can be selected by referring to past execution results. Some or all of the above processes in the execution unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the execution unit inputs past execution results into the generating AI, and the generating AI selects the optimal execution method.

[0086] The execution unit can customize the execution method based on the user's current work situation when executing a script. For example, if the user is busy, the execution unit provides a means that can be executed quickly by the generating AI. If the user is relaxed, the execution unit provides an execution method that includes detailed instructions by the generating AI. The execution unit understands the user's work situation in real time, and the generating AI selects the appropriate execution method. This enables efficient execution by customizing the execution method based on the user's work situation. Some or all of the above processing in the execution unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the execution unit inputs the user's work situation into the generating AI, and the generating AI selects the optimal execution method.

[0087] The execution unit can estimate the user's emotions and determine the priority of scripts to execute based on the estimated emotions. For example, if the user is stressed, the generation AI will postpone less important scripts and prioritize simple ones. If the user is relaxed, the generation AI will prioritize executing high-importance scripts. If the user is in a hurry, the generation AI will immediately execute urgent scripts. This allows for a more appropriate response by prioritizing scripts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using or without the generation AI. For example, the execution unit inputs user emotion data into the generation AI, which estimates the emotions and determines the priority of scripts.

[0088] The execution unit can select the optimal execution method when executing a script, taking into account the user's geographical location information. For example, if the user is in the office, the generating AI will prioritize executing scripts related to office work. If the user is out of the office, the generating AI will prioritize executing scripts related to work performed outside the office. Based on the user's geographical location information, the generating AI will prioritize executing scripts required at that location. In this way, the optimal execution method can be selected by taking the user's geographical location information into consideration. Some or all of the above processing in the execution unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the execution unit inputs the user's geographical location information into the generating AI, and the generating AI selects the optimal execution method.

[0089] The execution unit can analyze the user's social media activity and suggest execution methods when executing a script. For example, the execution unit uses the user's social media activity to determine the user's current interests, and the generating AI prioritizes the execution of relevant scripts. The execution unit uses the information shared by the user on social media to execute relevant business scripts using the generating AI. The execution unit analyzes the user's social media activity, and the generating AI executes scripts related to that activity. In this way, by analyzing the user's social media activity, it can suggest highly relevant execution methods. Some or all of the above processing in the execution unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the execution unit inputs the user's social media activity into the generating AI, and the generating AI suggests the optimal execution method.

[0090] The service provider can estimate the user's emotions and adjust the method of providing the results based on the estimated emotions. For example, if the user is relaxed, the service provider's generating AI will provide detailed results. If the user is in a hurry, the service provider's generating AI will provide concise and easily understandable results. If the user is stressed, the service provider's generating AI will provide simple and easy-to-understand results. This allows for more appropriate delivery by adjusting the method of providing results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using the generating AI or not. For example, the service provider inputs user emotion data into the generating AI, which estimates the emotions and adjusts the method of providing the results.

[0091] The service provider can select the optimal service method by referring to the user's past operation history when providing execution results. For example, the service provider's generating AI selects the optimal service method based on the service method the user has preferred to use in the past. The service provider's generating AI selects a service method suitable for a specific time period from the user's past operation history. The service provider analyzes the user's past operation history, and the generating AI provides a service method tailored to the user's preferences. This allows the service provider to select the optimal service method by referring to the user's past operation history. Some or all of the above-described processes in the service provider may be performed using the generating AI, or they may be performed without the generating AI. For example, the service provider inputs the user's past operation history into the generating AI, and the generating AI selects the optimal service method.

[0092] The delivery unit can customize the means of delivery based on the user's current work situation when providing execution results. For example, if the user is busy, the delivery unit provides a means of delivery that the generating AI can quickly understand. If the user is relaxed, the delivery unit provides a means of delivery that includes detailed instructions. The delivery unit grasps the user's work situation in real time, and the generating AI selects the appropriate means of delivery. This enables efficient delivery by customizing the means of delivery based on the user's work situation. Some or all of the above processing in the delivery unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the delivery unit inputs the user's work situation into the generating AI, and the generating AI selects the optimal means of delivery.

[0093] The service provider can estimate the user's emotions and prioritize the execution results based on the estimated emotions. For example, if the user is stressed, the service provider's generating AI will postpone less important execution results and prioritize simple ones. If the user is relaxed, the service provider's generating AI will prioritize providing high-importance execution results. If the user is in a hurry, the service provider's generating AI will immediately provide urgent execution results. This allows for a more appropriate response by prioritizing execution results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generating AI. For example, the service provider inputs user emotion data into a generating AI, which estimates the emotions and determines the priority of execution results.

[0094] The service provider can select the optimal delivery method when providing execution results, taking into account the user's geographical location information. For example, if the user is in the office, the generating AI will prioritize providing execution results related to office work. If the user is out of the office, the generating AI will prioritize providing execution results related to work performed while out of the office. Based on the user's geographical location information, the generating AI will prioritize providing execution results required at that location. This allows the service provider to select the optimal delivery method by taking the user's geographical location information into consideration. Some or all of the above processing in the service provider may be performed using the generating AI, or it may be performed without using the generating AI. For example, the service provider can input the user's geographical location information into the generating AI, and the generating AI will select the optimal delivery method.

[0095] The service provider can analyze the user's social media activity and propose a means of delivery when providing execution results. For example, the service provider's generating AI can understand the user's current interests from their social media activity and prioritize providing relevant execution results. The service provider's generating AI provides relevant business execution results based on information shared by the user on social media. The service provider analyzes the user's social media activity and the generating AI provides execution results related to that activity. In this way, by analyzing the user's social media activity, it is possible to propose a highly relevant means of delivery. Some or all of the above processing in the service provider may be performed using the generating AI or not. For example, the service provider inputs the user's social media activity into the generating AI, and the generating AI proposes the optimal means of delivery.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The office automation system can further include a biometric information acquisition unit that obtains the user's biometric information. The biometric information acquisition unit, for example, measures the user's heart rate and skin temperature to estimate the user's stress level. Based on the acquired biometric information, the generation unit can generate a simpler, less burdensome script if the user's stress level is high. The execution unit can execute a script containing detailed instructions if the user's stress level is low. This enables the generation and execution of more appropriate scripts based on the user's biometric information.

[0098] The office automation system can also include a history analysis unit that analyzes the user's past work history. For example, the history analysis unit analyzes patterns in the user's past work, and the generation unit generates the optimal script based on those patterns. The execution unit can select the most efficient execution method based on the past work history. The provision unit can provide optimal feedback to the user, referencing the past work history. This enables more efficient business automation by leveraging past work history.

[0099] The office automation system can further estimate the user's emotions and adjust the content of voice instructions based on those emotions. For example, if the user is stressed, the reception unit will prioritize receiving simple and easy-to-understand voice instructions generated by the AI. If the user is relaxed, the generation unit can generate a script with detailed instructions. If the user is in a hurry, the delivery unit can provide quick and easy-to-understand feedback. This allows for more appropriate responses by adjusting the content of voice instructions according to the user's emotions.

[0100] The office automation system can further include a location information acquisition unit that obtains the user's geographical location information. For example, if the user is in the office, the location information acquisition unit can prioritize generating scripts related to office tasks using AI. If the user is out of the office, the execution unit can prioritize executing scripts related to tasks performed outside the office. The provision unit can provide optimal feedback based on the user's geographical location information. This enables more appropriate task automation by taking the user's geographical location into consideration.

[0101] The office automation system can further estimate the user's emotions and adjust how the script is modified based on those emotions. For example, if the user is stressed, the generation unit can provide a simple and easy-to-understand modification method. If the user is relaxed, the execution unit can provide a modification method with detailed instructions. If the user is in a hurry, the delivery unit can provide a quickly understandable modification result. This allows for more appropriate responses by adjusting the script modification method according to the user's emotions.

[0102] The office automation system can also include a work status monitoring unit that grasps the user's work status in real time. For example, if the user is busy, the AI ​​generates a script that can be executed quickly. The execution unit can then execute the script, including detailed instructions, when the user is relaxed. The delivery unit can provide optimal feedback based on the user's work status. This enables more appropriate task automation according to the user's work situation.

[0103] The office automation system can further estimate the user's emotions and adjust the execution order of scripts based on those emotions. For example, if the user is stressed, the execution unit will prioritize simpler scripts and postpone less important ones. If the user is relaxed, the execution unit can prioritize executing more important scripts. If the user is in a hurry, the delivery unit can immediately execute urgent scripts. This allows for more appropriate responses by adjusting the script execution order according to the user's emotions.

[0104] The office automation system can also include a social media analysis unit that analyzes users' social media activity. For example, the social media analysis unit uses a generation AI to understand users' current interests from their social media activity and generate relevant scripts. The execution unit can then execute the relevant business scripts based on the information shared by the user on social media. The delivery unit can analyze users' social media activity and provide optimal feedback. This enables more appropriate business automation by leveraging users' social media activity.

[0105] The office automation system can further estimate the user's emotions and adjust how the results are delivered based on those emotions. For example, if the user is relaxed, the AI ​​can provide detailed results. If the user is in a hurry, the system can provide concise and easily understandable results. If the user is stressed, the system can provide simple and easy-to-understand results. This allows for more appropriate results delivery by adjusting how they are delivered according to the user's emotions.

[0106] The office automation system can also include an operation history analysis unit that analyzes the user's past operation history. For example, the operation history analysis unit uses a generating AI to select the optimal delivery method based on the user's preferred delivery methods in the past. The execution unit can select an execution method suitable for a specific time period based on the user's past operation history. The delivery unit can analyze the user's past operation history and provide optimal feedback. This enables more efficient business automation by leveraging the user's past operation history.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives voice instructions. For example, a user can give a voice instruction such as, "Summarize the data in the spreadsheet and create a graph." The reception desk uses speech recognition technology to convert the voice instructions into text data. Step 2: The generation unit analyzes the voice instructions received by the reception unit and generates a script. For example, the generation unit uses a generation AI to analyze the voice instructions and generate a script that aggregates data from a spreadsheet and creates a graph. The generation unit generates a script when the generation AI receives the prompt, "Aggregate the data from the spreadsheet and create a graph." Step 3: The execution unit executes the script generated by the generation unit. For example, the execution unit executes the generated script, aggregates the data in the spreadsheet, and creates a graph. Step 4: The provider unit provides the results executed by the execution unit. For example, the provider unit displays the execution results of the generated script to the user. The provider unit analyzes the execution results using the generation AI and provides feedback to the user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reception unit, generation unit, execution unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the microphone 38B and control unit 46A of the smart device 14 and receives voice instructions from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the voice instructions to generate a script. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and executes the generated script. The provision unit is implemented by the display 40A and speaker 40B of the smart device 14 and provides the execution results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, generation unit, execution unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214 and receives voice instructions from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the voice instructions to generate a script. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and executes the generated script. The provision unit is implemented by the display and speaker 240 of the smart glasses 214 and provides the execution results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, generation unit, execution unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314 and receives voice instructions from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the voice instructions to generate a script. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and executes the generated script. The provision unit is implemented by the display 343 and speaker 240 of the headset terminal 314 and provides the execution results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, generation unit, execution unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the robot 414 and receives voice instructions from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the voice instructions to generate a script. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and executes the generated script. The provision unit is implemented by the display and speaker 240 of the robot 414 and provides the execution results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A reception desk that accepts voice commands, A generation unit analyzes the voice instructions received by the reception unit and generates a script, An execution unit that executes the script generated by the generation unit, The system comprises a providing unit that provides the results of the execution performed by the execution unit. A system characterized by the following features. (Note 2) The generating unit is Generate a script that aggregates data from a spreadsheet and creates graphs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It includes a modification section that modifies the script based on user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It is equipped with an additional instruction receiving unit to receive additional instructions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the user with the results of running the generated script. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice command acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system analyzes the user's past voice command history and selects the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving voice commands, filtering is performed based on the user's current work status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice commands to accept based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving voice commands, the system prioritizes receiving commands that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving voice commands, the system analyzes the user's social media activity and accepts relevant commands. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts how the script is generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a script, adjust the level of detail based on the importance of the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating scripts, different generation algorithms are applied depending on the category of instructions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the script generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating scripts, the generation priority is determined based on when the instructions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating scripts, adjust the generation order based on the relevance of the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The execution unit is, It estimates the user's emotions and adjusts how the script is executed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The execution unit is, When a script is executed, the system refers to past execution results to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The execution unit is, When a script is executed, the execution method is customized based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The execution unit is, It estimates the user's emotions and determines the priority of scripts to execute based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The execution unit is, When executing a script, the system will select the optimal execution method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The execution unit is, When the script is executed, it analyzes the user's social media activity and suggests the appropriate course of action. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the results are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing execution results, the system will refer to the user's past operation history to select the most suitable method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing execution results, the method of delivery will be customized based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the execution results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing execution results, the optimal delivery method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the execution results, we will analyze the user's social media activity and propose a method for providing the results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts voice commands, A generation unit analyzes the voice instructions received by the reception unit and generates a script, An execution unit that executes the script generated by the generation unit, The system comprises a providing unit that provides the results of the execution performed by the execution unit. A system characterized by the following features.

2. The generating unit is Generate a script that aggregates data from a spreadsheet and creates graphs. The system according to feature 1.

3. The generating unit is It includes a modification section that modifies the script based on user instructions. The system according to feature 1.

4. The aforementioned reception unit is It is equipped with an additional instruction receiving unit to receive additional instructions. The system according to feature 1.

5. The aforementioned supply unit is, Provide the user with the results of running the generated script. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice command acceptance based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is The system analyzes the user's past voice command history and selects the optimal reception method. The system according to feature 1.

8. The aforementioned reception unit is When receiving voice commands, filtering is performed based on the user's current work status and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice commands to accept based on the estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving voice commands, the system prioritizes receiving commands that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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